2021
DOI: 10.1109/access.2021.3061716
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3D Hand Pose Estimation via Graph-Based Reasoning

Abstract: Hand pose estimation from a single depth image has recently received significant attention owing to its importance in many applications requiring human-computer interaction. The rapid progress of convolutional neural networks (CNNs) and technological advances in low-cost depth cameras have greatly improved the performance of the hand pose estimation method. Nevertheless, regressing joint coordinates is still a challenging task due to joint flexibility and self-occlusion. Previous hand pose estimation methods h… Show more

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Cited by 5 publications
(1 citation statement)
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“…Compared with image-based methods, hand skeletonbased methods mitigate the interference of complex backgrounds and various lighting conditions and are more robust while reducing computational costs and enabling realtime gesture interaction on mobile devices. Moreover, due to the development of low-cost depth cameras (i.e., Microsoft Kinect [14]or Intel RealSense [15]) and hand pose estimation algorithms [16]- [18], correct hand skeleton sequences can be easily obtained. These advantages have promoted skeletonbased gesture recognition in recent studies.…”
Section: Introductionmentioning
confidence: 99%
“…Compared with image-based methods, hand skeletonbased methods mitigate the interference of complex backgrounds and various lighting conditions and are more robust while reducing computational costs and enabling realtime gesture interaction on mobile devices. Moreover, due to the development of low-cost depth cameras (i.e., Microsoft Kinect [14]or Intel RealSense [15]) and hand pose estimation algorithms [16]- [18], correct hand skeleton sequences can be easily obtained. These advantages have promoted skeletonbased gesture recognition in recent studies.…”
Section: Introductionmentioning
confidence: 99%